Artificial Intelligence and Healthcare Policy: A Bibliometric Analysis of Global Research Trends.

Background: Artificial intelligence is increasingly influencing health care and policy, yet the global research landscape linking artificial intelligence and health care policy remains underexplored. This study aimed to map publication trends, major contributors, collaboration networks, citation structures, and emerging themes in this field. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection. The search was performed on 3 May 2026, and covered publications from 2000 to 3 May 2026. The final dataset included 347 peer-reviewed English-language original research and review articles. Biblioshiny, VOSviewer, and CiteSpace were used to analyze publication trends, country and institutional contributions, author and journal productivity, collaboration networks, citation and co-citation structures, keyword patterns, and thematic evolution. Results: Publications increased markedly after 2020 and reached their highest annual output in 2025. The 2026 publication count was lower because data for that year were partial at the time of database retrieval. Researchers from 82 countries and 900 institutions contributed to the field, with the United States leading in output, followed by China, England, Canada, and India. Harvard Medical School was the most productive institution, whereas Harvard University had the highest institutional centrality. Frontiers in Public Health published the most articles, and PLOS ONE was the most frequently co-cited journal. The most cited article was "Artificial intelligence and the future of global health." Key research themes included machine learning, COVID-19, health policy, risk, large language models, interpretable machine learning, neural network-assisted screening, socioeconomic perspectives, and public health applications. Conclusions: Research on artificial intelligence and health care policy has expanded rapidly, particularly in recent years, and is increasingly centered on predictive modeling, public health decision-making, and emerging artificial intelligence technologies. These findings highlight influential contributors, evolving themes, and future directions for researchers, policymakers, and health care leaders.